Speech Compression. Introduction Use of multimedia in personal computers Requirement of more disk space Also telephone system requires compression Topics.

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Presentation on theme: "Speech Compression. Introduction Use of multimedia in personal computers Requirement of more disk space Also telephone system requires compression Topics."— Presentation transcript:

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Sampling Variables The sample resolution is simply a measure of how accurately the digital sample can measure the voltage it is recording Quantization error Depending on quantization error, your output has some distortions Crash of drums in an orchestra  500mv Delicate violin solo may never go outside 5mv

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If sampling rate is much slower, output is not desired Output of slower sampling rate

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Human ear capacity  20kHz So sampling rate must be 40kHz or more In CD, 44kHz using 10-bit samples Digital phone’s sampling rate is 8kHz Quality of sound – Mathematically very hard to measure – Based on listeners

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PC Based Sound Early PCs  no sound PC speakers has only one bit sound  beep or buzz Sounds require higher disk space so higher compression ratio and higher processing power needed. Cost of processing is decreasing fast compare to cost of transmission lines

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Lossless Compression of Sound Generally not used for sound Bandwidth is fix for transmission lines A sound sample that is easy to compress

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A sound sample that is difficult to compress Lossless compression can not give batter results

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Lossy Compression Certain loss in fidelity Lossy compression followed by lossless compression Lossy compression frequently smooths out the data, which makes it even more suitable for lossless compression

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Silence Compression Equivalent of RLE on normal data files It is lossy technique A sound sample with a long sequence of silence

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Parameters of silence compression prog. – Threshold value of silence – Run of silence – Threshold for recognizing the start of a run of silence (generally 4) – Threshold for recognizing the stop of silence (generally 2 or 3) Mainly it converts noisy silence to pure silence It provide an excellent way to remove redundancy from sound files

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Companding We need higher sampling resolution to cover all sound samples. But it generate higher data rate And if it is lower, small values are considered as silence To solve this, telecommunication industry use non-linear matched set of ADCs and DACs. Normal ADC (used in PCs) uses linear conversion schemes

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Normal ADC and DAC – Code change from 0 to 1, voltage change from 0mv to 1mv – Code change from 100 to 101, voltage change from 100mv to 101mv Today’s digital telecommunication equipment are using companding codec It does not use standard linear function but uses exponential function Resolution of smaller codec values are much finer then larger ones

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Non-linear ADC and DAC – Code change from 0 to 1, the difference would be 1mv – Code change from 100 to 101, the difference would be 10mv This will give effective range of 13-bit codecs by using only 8-bit samples Example Input code01234567 Output value01328446281103127

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This algorithm does an excellent job of compressing sound without damaging quality Both compression and decompression can take place via lookup table, so faster processing Amount of compression is known in advance and does not depend on input file Algorithm can be tuned to any desired compression ratio